Such a method is, e.g., known from Guo et al., ‘Development and Application of Functional Databases for Planning Deep-Brain Neurosurgical Procedures’. From Guo et al. it is known to fuse pre-operative MR images of individual patients with digitized anatomical atlases. From the MR images alone it is very difficult or even impossible to visually distinguish small different neurological structures such as the subthalamic nucleus (STN) which is often targeted during deep brain stimulation procedures. When using additional information from anatomic atlases and/or probabilistic functional atlases, it is possible to enhance the accuracy and precision for localizing surgical targets from this pre-operative surgical planning. In Guo et al. probabilistic functional atlases based on additional standardized electrophysiological information available prior to surgery are used to assist the surgical target determination and to provide anatomical labeling to the 3D image data. The fused images and the standardized electrophysiological information together serve to enable display of the position of a probe needle along a real or simulated surgical trajectory. The position of the probe needle is displayed in a 3D image volume and in 2D slices of a patient.
However, registration of the atlas to pre-operative MR images has limited precision due to registration errors inherent to any registration method. Image-processing such as segmentation may be utilized to further improve the anatomic labeling. However, also this technique has inherent limited accuracies. Further, during surgery an implanted probe may unintentionally deviate from a planned trajectory (e.g. due to an imperfection such as a slight bending of the probe) or the anatomy may slightly shift due to the surgical procedure itself (e.g. the implantation of the probe in the tissue generates a small force field that pushes aside the tissue; loss of cerebrospinal fluid during the surgery may lead to pressure-changes in the tissue resulting in larger deformations referred to as ‘brain-shift’). Precision implantation of a probe in a small target therefore usually requires intra-operative measurements (e.g. electrophysiological measurements) to exactly pinpoint the target and to correct for errors due to inaccuracies in the pre-operative planning or due to anatomic shifts during surgery or due to (unintended) deviations of the probe from the planned trajectory. Correct interpretation of such intra-operative measurements is crucial to accurate target localization but may be a very difficult analysis to perform due to the complexity of the data and the amount of information needed to be processed by experts when performing this analysis.
It is a problem of the method described in Guo et al. that the resulting data does not provide sufficiently clear and unambiguous information for the surgeon, which information could help the physician with recognizing the types of neurological structures to expect and with localizing a surgical target along the planned and traversed surgical paths.